Bibliographic record
Abstract
We congratulate Hyuna Sung and colleagues1Sung H Siegel RL Rosenberg PS Jemal A Emerging cancer trends among young adults in the USA: analysis of a population-based cancer registry.Lancet Public Health. 2019; 4: e137-e147Summary Full Text Full Text PDF PubMed Scopus (244) Google Scholar for their analyses of cancer trends among young adults in the USA.1Sung H Siegel RL Rosenberg PS Jemal A Emerging cancer trends among young adults in the USA: analysis of a population-based cancer registry.Lancet Public Health. 2019; 4: e137-e147Summary Full Text Full Text PDF PubMed Scopus (244) Google Scholar The authors observed an increase in the incidence of several obesity-related cancers in this population. However, they might have overlooked the effect of cancer surveillance bias on these trends. Surveillance bias occurs when a condition is searched with differential intensity across populations or over time, or according to care setting and patient characteristics.2Haut ER Pronovost PJ Surveillance bias in outcomes reporting.JAMA. 2011; 305: 2462-2463Crossref PubMed Scopus (195) Google Scholar Several types of cancer are highly sensitive to the intensity of screening and clinical detection activities, such as prostate, thyroid, and kidney cancers; these cancers have a substantial reservoir of indolent, subclinical forms and are at high risk of being overdiagnosed.3Welch HG Brawley OW Scrutiny-dependent cancer and self-fulfilling risk factors.Ann Intern Med. 2018; 169: 134-135Crossref PubMed Scopus (0) Google Scholar, 4Chiolero A Santschi V Paccaud F Public health surveillance with electronic medical records: at risk of surveillance bias and overdiagnosis.Eur J Public Health. 2013; 23: 350-351Crossref PubMed Scopus (22) Google Scholar One major consequence is that changes in the incidence of such scrutiny-dependent cancers do not simply reveal the effect of changes in carcinogenic exposures; they also result from changes in the frequency and modality of screening and detection activities.2Haut ER Pronovost PJ Surveillance bias in outcomes reporting.JAMA. 2011; 305: 2462-2463Crossref PubMed Scopus (195) Google Scholar Based on the ecological association between obesity and cancer trends, arguing that obesity would be the cause of the increased incidence of cancer among young adults is highly disputable. Nevertheless, since obesity is associated with a greater frequency of medical examinations,5Bertakis KD Azari R Obesity and the use of health care services.Obes Res. 2005; 13: 372-379Crossref PubMed Scopus (102) Google Scholar the probability of detecting scrutiny-dependent cancers could be higher among obese individuals. If we assume differential secular trends in cancer detection intensity by age, the parallel rise in obesity and several types of cancer among young adults could be merely the result of a surveillance bias. I declare no competing interests. Cancer surveillance, obesity, and potential biasAlthough Hyuna Sung and colleagues1 stressed caution in interpreting their ecological study in The Lancet Public Health (March, 2019), the naive reader—or the media, as was the case2—might conclude that obesity is fuelling the reported disproportionate temporal increases in incidence of obesity-related cancers in young adults. However, there are many arguments against obesity as a causal driver. Full-Text PDF Open AccessEmerging cancer trends among young adults in the USA: analysis of a population-based cancer registryThe risk of developing an obesity-related cancer seems to be increasing in a stepwise manner in successively younger birth cohorts in the USA. Further studies are needed to elucidate exposures responsible for these emerging trends, including excess bodyweight and other risk factors. Full-Text PDF Open Access
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".